Multilayer neural network with multi-valued neurons in time series forecasting of oil production

In this paper, we discuss the long-term time series forecasting using a Multilayer Neural Network with Multi-Valued Neurons (MLMVN). This is complex-valued neural network with a derivative-free backpropagation learning algorithm. We evaluate the proposed approach using a real-world data set describing the dynamic behavior of an oilfield asset located in the coastal swamps of the Gulf of Mexico. We show that MLMVN can be efficiently applied to univariate and multivariate multi-step ahead prediction of reservoir dynamics. This paper is not only intended for proposing a novel model of forecasting but to study carefully several aspects of the application of ANN models to time series forecasting that could be of the interest for pattern recognition community.